Multi-chip dataflow architecture for massive scale biophysically accurate neuron simulation

The ability to simulate brain neurons in real-time using biophysically-meaningful models is a critical pre-requisite grasping human brain behavior. By simulating neurons' behavior, it is possible, for example, to reduce the need for in-vivo experimentation, to improve artificial intelligence and to replace damaged brain parts in patients. A biophysically accurate but complex neuron model, which can be used for such applications, is the Hodgkin-Huxley (HH) model. State of the art simulators are capable of simulating, in real-time, tens of neurons, at most. The currently most advanced simulator is able to simulate 96 HH neurons in real-time. This simulator is limited by its exponential growth in communication costs. To overcome this problem, in this thesis, we propose a new system architecture, which massively increases the amount of neurons which is possible to simulate. By localizing communications, the communication cost is reduced from an exponential to a linear growth with the number of simulated neurons As a result, the proposed system allows the simulation of over 3000 to 19200 cells (depending on the connectivity scheme). To further increase the number of simulated neurons, the proposed system is designed in such a way that it is possible to implement it over multiple chips. Experimental results have shown that it is possible to use up to 8 chips and still keeping the communication costs linear with the number of simulated neurons. The systems is very flexible and allows to tune, during run-time, various parameters, including the presence of connections between neurons, eliminating (or reducing) resynthesis costs, which turn into much faster experimentation cycles. All parts of the system are generated automatically, based on the neuron connectivity scheme. A powerful simulator that incorporates latencies for on and off chip communication, as well as calculation latencies, can be used to find the right configuration for a particular task. As a result, the resulting highly adaptive and configurable system allows for biophysically-accurate simulation of massive amounts of cells.

Subject

Inferior Olivary Nucleus
systemc
simulation
biologically accurate
massive scale
brain modelling
Spiking Neural Network
linear scalability

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Student theses

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master thesis

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